In social media, classical features can be extracted by using stylistic signals from text such as bag of n-grams, char-grams, part-of-speech labels, and lemmas [1]}, structural signals such as hashtags, mentions, uppercase characters, punctuation marks, and the length of the tweet [2]}, [3]}, and pragmatic signals related to author's profile [4]}. With modern deep learning models, there is shift towards contextualised representations using word vector representation algorithms, either by having personalised language models trained on task specific language or as a pre-trained language model offered after training using complex architecture and billions of documents. Using deep learning layers as automated feature engineering methods can be implemented to train the model afterwards. In [5]}, they utilized Bidirectional Conditional Encoding using LSTM achieving state-of-the-art results on stance detection task. Recently, there is a resurgence of research in incorporating network homophily [6]} to represent social interactions within a network. Moreover, Knowledge graphs [7]} can in turn represent these complex network relationships (e.g. authors friendships) as simple embedded vectors sampled considering the nodes and weighted edges within the network complexity structure.
